{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 案例：电商网站订单数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 商业模式（分类）\n",
    "\n",
    "- B2B：商家对商家（企业卖家对企业买家），交易双方都是企业，最典型的案例就是阿里巴巴，汇聚了各行业的供应商，特点是订单量一般较大。\n",
    "- B2C：商家对个人（企业卖家对个人买家），例如：唯品会，聚美优品。\n",
    "- B2B2C：商家对商家对个人，例如：天猫、京东。\n",
    "- C2C：个人（卖家）对个人（买家），例如：淘宝、人人车。\n",
    "- O2O：线上（售卖）到线下（提货），将线下的商务机会与互联网结合，让互联网成为线下交易的平台，例如：美团、饿了么。\n",
    "- C2B：个人对商家（个人买家对企业卖家），先有消费者提出需求，后有商家按需求组织生产，例如：尚品宅配。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 生命周期\n",
    "\n",
    "互联网和电商刚刚兴起的时候，只要吸引用户打开网页进入店面，就会有一定比例的人下单，持续产生转化。在电商的“流量红利时代”，用“$ 店铺访客 \\times 购买转化率 $”就可以估算出买家数，再用“$ 买家数 \\times 客单价 $”就可以估算成交额。\n",
    "\n",
    "随着电商的普及，拉新越来越困难（流量下滑、市场下沉、会员绑定），获客成本越来越高，用户也变得更加挑剔。电商行业不得不从“流量运营”转变为“精细化用户运营”，更加注重促成单个用户的多次购买以及老用户带动新用户的社交裂变。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 核心指标\n",
    "\n",
    "指标，具备业务含义，能够反映业务特征的数据，有几个要点：\n",
    "\n",
    "1. 指标必须是数值，不能是文本、日期等。\n",
    "2. 指标都是汇总计算出来的，单个明细数据不是指标。\n",
    "\n",
    "- 新老用户\n",
    "    - 新（老）用户数量占比\n",
    "    - 新（老）用户金额占比\n",
    "    \n",
    "- 复购率和回购率\n",
    "    - 复购率：复购（某段时间有2次及以上购买行为）用户的占比。复购率能反映用户的忠诚度，监测周期一般较长。\n",
    "    - 回购率：回购率一般监测周期较短，可以反映如短期促销活动对用户的吸引力。\n",
    "    \n",
    "- 用户交易常用指标\n",
    "    - 访问次数（PV）：一定时间内某个页面的浏览次数。\n",
    "    - 访问人数（UV）：一定时间内访问某个页面的人数。\n",
    "    - 加购数：将某款商品加入到购物车的用户数。\n",
    "    - 收藏数：收藏某款商品的用户数。\n",
    "    - GMV（总交易额、成交总额）：Gross Merchandise Volume，通常说的交易“流水”。\n",
    "    - 客单价（ARPU）：“$ 总收入 / 总用户数 $” ---> ARPPU。\n",
    "    - 转化率：“$ 付费用户数 / 访客数 $”。\n",
    "    - 折扣率：“$ GMV / 吊牌总额 $”，其中吊牌总额为：“$ 吊牌价 \\times 销量 $”。\n",
    "    - 拒退量：拒收和退货的总数量。\n",
    "    - 拒退额：拒收和退货的总金额。\n",
    "    - 实际销售额：“$ GMV - 拒退额 $”。\n",
    "    \n",
    "- 商品管理常用指标\n",
    "    - SPU数：Standard Product Unit。\n",
    "    - SKU数：Standard Keeping Unit。\n",
    "    - 售卖比：“$ GMV / 备货值 $”，了解商品流转情况，可以用于库存优化。\n",
    "    - 动销率：“$ 有销量的SKU数 / 在售SKU数 $”。\n",
    "    \n",
    "    ![](spu-and-sku.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 指标体系\n",
    "\n",
    "一个指标没有办法解决复杂的业务问题，需要使用多个指标从不同维度来评估业务，所以指标体系是根据运营的目标，将多个指标创建联系后形成的整体。\n",
    "\n",
    "<img src=\"indicator-system.png\" width=\"600\">\n",
    "\n",
    "指标体系的作用：\n",
    "\n",
    "1. 监控业务状况。\n",
    "2. 通过拆解指标找到问题。\n",
    "3. 评估业务值得改进的地方。\n",
    "\n",
    "建立指标体系的步骤：\n",
    "\n",
    "1. 厘清业务阶段和方向。\n",
    "    - 创业期：关注盘子大小，用户体量，指标围绕用户量提升做各种维度的拆解。\n",
    "    - 发展期：关注产品健康度，优化当前用户量结构，提升留存率。\n",
    "    - 成熟期：关注市场份额，关注和收入相关的指标，做好市场份额和竞品的监控。\n",
    "2. 确定核心指标。\n",
    "    - 通过KPI/OKR，找到一级指标（北极星指标及其伴随指标）。\n",
    "    <img src=\"polaris-indicator.png\" width=\"600\">\n",
    "    - 了解业务运营状况，找到二级指标。\n",
    "    - 梳理业务流程，找到三级指标。\n",
    "3. 指标维度拆解。\n",
    "    <img src=\"disassemble.png\" width=\"600\">\n",
    "4. 指标的宣贯和落地。\n",
    "    - 业务埋点\n",
    "    - 创建报表\n",
    "    - 统一口径\n",
    "    - 更新周期\n",
    "\n",
    "指标体系搭建常见问题：\n",
    "\n",
    "1. 没有北极星指标，抓不住重点。\n",
    "2. 指标间没有逻辑联系，找不出问题。\n",
    "3. 指标缺乏业务意义，为了拆解而拆解。\n",
    "4. 缺乏和业务方的沟通。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分析方法\n",
    "\n",
    "1. 对比分析法\n",
    "2. 相关分析法\n",
    "3. RFM模型分析法\n",
    "4. AARRR模型分析法\n",
    "5. 漏斗分析法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 代码实操\n",
    "\n",
    "1. 提取2019年的订单数据\n",
    "2. 处理业务流程不符的数据（支付时间早于下单时间、支付时长超过30分钟、订单金额小于0、支付金额小于0）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分析\n",
    "\n",
    "6. 交易总金额（GMV）、总销售额、实际销售额、退货率、客单价\n",
    "7. 每月GMV及趋势分析（折线图）\n",
    "8. 流量渠道来源分析（饼图）\n",
    "9. 周一到周日哪天的下单量最高、每天哪个时段下单量最高（柱状图）\n",
    "10. 用户复购率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GMV: 108495929.7\n",
      "总销售额: 102600896.62845254\n",
      "实际销售额: 88915993.09978105\n",
      "退货率: 0.13173507805770365\n",
      "客单价(Arppu): 1130.75759976322\n"
     ]
    }
   ],
   "source": [
    "# 计算交易总金额（GMV）、总销售额、实际销售额、退货率、客单价（ARPPU）\n",
    "gmv = order_df.orderAmount.sum()\n",
    "total_payment = order_df.payment.sum()\n",
    "real_payment = order_df[order_df.chargeback == '否'].payment.sum()\n",
    "back_rate = order_df[order_df.chargeback == '是'].orderID.count() / order_df.shape[0]\n",
    "arppu = real_payment / order_df.userID.nunique()\n",
    "print('GMV:', gmv)\n",
    "print('总销售额:', total_payment)\n",
    "print('实际销售额:', real_payment)\n",
    "print('退货率:', back_rate)\n",
    "print('客单价(Arppu):', arppu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 每月GMV及趋势分析\n",
    "order_df['month'] = order_df.orderTime.dt.month\n",
    "gmv_by_month = order_df.groupby('month').orderAmount.sum() / 10000\n",
    "# print(gmv_by_month.max())\n",
    "pay_by_month = order_df.groupby('month').payment.sum() / 10000\n",
    "real_by_month = order_df[order_df.chargeback == '否'].groupby('month').payment.sum() / 10000\n",
    "# print(real_by_month.min())\n",
    "# 创建画布\n",
    "plt.figure(figsize=(8, 4), dpi=120)\n",
    "# 绘制折线图\n",
    "plt.plot(gmv_by_month.index, gmv_by_month, color='red',label='GMV')\n",
    "plt.plot(pay_by_month.index, pay_by_month, color='green', label='总销售额')\n",
    "plt.plot(real_by_month.index, real_by_month, marker='x', color='blue',label='实际销售额')\n",
    "# 定制横轴和纵轴的刻度\n",
    "plt.xticks(gmv_by_month.index, labels=[f'{x}月' for x in gmv_by_month.index])\n",
    "plt.yticks(np.arange(450, 1101, 100))\n",
    "# 定制横轴和纵轴标签\n",
    "plt.xlabel('月份')\n",
    "plt.ylabel('金额（万元）')\n",
    "# 定制图表的标题\n",
    "plt.title('按月GMV统计')\n",
    "# 设置显示图例和网格线\n",
    "plt.legend(loc='lower right')\n",
    "plt.grid(axis='y', alpha=0.5, linestyle='--')\n",
    "# 在图上指定的位置添加文本\n",
    "for i in real_by_month.index:\n",
    "    plt.text(i, real_by_month[i] + 20, round(real_by_month[i], 2))\n",
    "# 显示图像\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# GMV流量渠道来源拆解\n",
    "gmv_by_channel = order_df.groupby('chanelID').orderAmount.sum() / 10000\n",
    "# plt.figure(figsize=(10,10))\n",
    "gmv_by_channel.nlargest(10).plot(\n",
    "    kind='pie',\n",
    "    figsize=(8, 8),\n",
    "    fontsize=16,\n",
    "    autopct='%.2f%%',\n",
    "    pctdistance=0.8,\n",
    "    wedgeprops={\n",
    "        'width':0.4,\n",
    "        'linewidth': 1,\n",
    "        'edgecolor': 'white'\n",
    "    }\n",
    ")\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 周一到周日哪天的下单量最高\n",
    "order_df['weekday'] = order_df.orderTime.dt.dayofweek\n",
    "ser = order_df.groupby('weekday').orderID.count()\n",
    "ser.plot(\n",
    "    figsize=(10, 4),\n",
    "    kind='bar',\n",
    "    fontsize=16,\n",
    "    color=np.random.rand(7, 3)\n",
    ")\n",
    "plt.xticks(ser.index, labels=[f'星期{x}' for x in '一二三四五六日'],\n",
    "          rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 每天哪个时段下单量最高\n",
    "order_df['time'] = order_df.orderTime.dt.floor('30T').dt.time\n",
    "ser = order_df.groupby('time').orderID.count()\n",
    "ser.plot(\n",
    "    figsize=(10, 5),\n",
    "    kind='bar',\n",
    "    fontsize=12,\n",
    "    color=np.random.rand(48, 3)\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>month</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>user-100000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100003</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100006</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100007</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100008</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100013</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100014</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100018</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100022</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100023</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100024</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100025</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100026</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100027</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100030</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100032</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100033</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100034</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100037</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100038</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100042</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100043</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100044</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100046</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100047</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100048</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100050</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100054</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100057</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100061</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299907</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299910</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299917</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299918</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299920</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299921</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299922</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299924</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299926</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299929</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299930</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299934</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299938</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299940</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299942</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299943</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299944</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299945</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299954</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299955</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299958</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299959</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299962</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299970</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299972</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299980</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299983</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299989</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299992</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299995</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78634 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "month         1    2    3    4    5    6    7    8    9    10   11   12\n",
       "userID                                                                 \n",
       "user-100000  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN\n",
       "user-100003  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "user-100006  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  1.0  NaN\n",
       "user-100007  1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "user-100008  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN\n",
       "user-100013  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN\n",
       "user-100014  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN\n",
       "user-100018  NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0\n",
       "user-100022  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN  1.0  NaN\n",
       "user-100023  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN\n",
       "user-100024  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "user-100025  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN\n",
       "user-100026  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN\n",
       "user-100027  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "user-100030  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN\n",
       "user-100032  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN  1.0  NaN  NaN  NaN\n",
       "user-100033  1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN\n",
       "user-100034  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN\n",
       "user-100037  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN\n",
       "user-100038  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "user-100042  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN\n",
       "user-100043  1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "user-100044  NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "user-100046  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN\n",
       "user-100047  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  2.0  NaN  NaN  NaN\n",
       "user-100048  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN\n",
       "user-100050  NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "user-100054  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0\n",
       "user-100057  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0\n",
       "user-100061  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0\n",
       "...          ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...\n",
       "user-299907  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "user-299910  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN  1.0\n",
       "user-299917  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN\n",
       "user-299918  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "user-299920  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN\n",
       "user-299921  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "user-299922  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN\n",
       "user-299924  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN\n",
       "user-299926  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN  1.0  NaN  NaN\n",
       "user-299929  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0\n",
       "user-299930  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  1.0  NaN  NaN\n",
       "user-299934  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0\n",
       "user-299938  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "user-299940  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "user-299942  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN\n",
       "user-299943  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "user-299944  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "user-299945  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN\n",
       "user-299954  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN\n",
       "user-299955  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "user-299958  NaN  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "user-299959  1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "user-299962  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN\n",
       "user-299970  NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "user-299972  1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "user-299980  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN  NaN\n",
       "user-299983  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0\n",
       "user-299989  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN  1.0  NaN\n",
       "user-299992  1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "user-299995  NaN  NaN  1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "\n",
       "[78634 rows x 12 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算用户复购率\n",
    "temp = pd.pivot_table(order_df, index='userID', columns='month',\n",
    "               values='orderID',\n",
    "              aggfunc='count')\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "def handle_data(x):\n",
    "    if x > 1:\n",
    "        return 1\n",
    "    elif x == 1:\n",
    "        return 0\n",
    "    return np.nan\n",
    "\n",
    "temp = temp.applymap(handle_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "1     0.018266\n",
       "2     0.011412\n",
       "3     0.017426\n",
       "4     0.020207\n",
       "5     0.030556\n",
       "6     0.030540\n",
       "7     0.025224\n",
       "8     0.031645\n",
       "9     0.024724\n",
       "10    0.027918\n",
       "11    0.029120\n",
       "12    0.030873\n",
       "dtype: float64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.sum() / temp.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据建模\n",
    "\n",
    "- RFM模型\n",
    "\n",
    "   - R：Recency - 最近一次购买 ---> 低\n",
    "   - F: Frequency - 购买的频次 ---> 高 \n",
    "   - M: Monetary - 购买总金额 ---> 高\n",
    "\n",
    "![](rfm-view.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F</th>\n",
       "      <th>orderAmount</th>\n",
       "      <th>orderTime</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>user-100000</th>\n",
       "      <td>1</td>\n",
       "      <td>1978.47</td>\n",
       "      <td>2019-10-13 18:46:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100003</th>\n",
       "      <td>1</td>\n",
       "      <td>521.60</td>\n",
       "      <td>2019-05-24 13:04:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100006</th>\n",
       "      <td>1</td>\n",
       "      <td>466.89</td>\n",
       "      <td>2019-11-14 15:37:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100007</th>\n",
       "      <td>1</td>\n",
       "      <td>2178.20</td>\n",
       "      <td>2019-01-14 18:45:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100008</th>\n",
       "      <td>1</td>\n",
       "      <td>4949.65</td>\n",
       "      <td>2019-11-16 17:15:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100013</th>\n",
       "      <td>1</td>\n",
       "      <td>565.26</td>\n",
       "      <td>2019-09-29 14:07:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100014</th>\n",
       "      <td>1</td>\n",
       "      <td>430.34</td>\n",
       "      <td>2019-07-30 12:04:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100018</th>\n",
       "      <td>1</td>\n",
       "      <td>453.83</td>\n",
       "      <td>2019-12-24 20:50:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100022</th>\n",
       "      <td>2</td>\n",
       "      <td>3899.20</td>\n",
       "      <td>2019-11-16 11:38:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100025</th>\n",
       "      <td>1</td>\n",
       "      <td>113.72</td>\n",
       "      <td>2019-07-21 14:38:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100027</th>\n",
       "      <td>1</td>\n",
       "      <td>924.18</td>\n",
       "      <td>2019-06-05 15:51:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100030</th>\n",
       "      <td>1</td>\n",
       "      <td>2428.86</td>\n",
       "      <td>2019-09-16 18:38:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100032</th>\n",
       "      <td>2</td>\n",
       "      <td>1662.67</td>\n",
       "      <td>2019-09-14 21:19:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100033</th>\n",
       "      <td>2</td>\n",
       "      <td>7573.07</td>\n",
       "      <td>2019-10-22 14:15:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100034</th>\n",
       "      <td>1</td>\n",
       "      <td>2567.65</td>\n",
       "      <td>2019-09-23 23:02:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100037</th>\n",
       "      <td>1</td>\n",
       "      <td>690.51</td>\n",
       "      <td>2019-10-12 20:56:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100038</th>\n",
       "      <td>1</td>\n",
       "      <td>562.78</td>\n",
       "      <td>2019-05-15 11:09:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100042</th>\n",
       "      <td>1</td>\n",
       "      <td>1284.35</td>\n",
       "      <td>2019-08-19 18:52:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100046</th>\n",
       "      <td>2</td>\n",
       "      <td>3030.89</td>\n",
       "      <td>2019-11-04 21:47:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100047</th>\n",
       "      <td>2</td>\n",
       "      <td>709.90</td>\n",
       "      <td>2019-09-14 14:56:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100048</th>\n",
       "      <td>1</td>\n",
       "      <td>743.85</td>\n",
       "      <td>2019-09-03 10:56:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100050</th>\n",
       "      <td>1</td>\n",
       "      <td>310.02</td>\n",
       "      <td>2019-03-11 08:25:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100054</th>\n",
       "      <td>1</td>\n",
       "      <td>953.79</td>\n",
       "      <td>2019-12-10 17:20:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100057</th>\n",
       "      <td>1</td>\n",
       "      <td>598.73</td>\n",
       "      <td>2019-12-04 14:27:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100062</th>\n",
       "      <td>1</td>\n",
       "      <td>496.58</td>\n",
       "      <td>2019-02-27 19:47:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100063</th>\n",
       "      <td>1</td>\n",
       "      <td>246.78</td>\n",
       "      <td>2019-02-21 15:36:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100065</th>\n",
       "      <td>1</td>\n",
       "      <td>1488.90</td>\n",
       "      <td>2019-06-14 23:39:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100067</th>\n",
       "      <td>1</td>\n",
       "      <td>1254.56</td>\n",
       "      <td>2019-12-17 23:23:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100068</th>\n",
       "      <td>1</td>\n",
       "      <td>3466.88</td>\n",
       "      <td>2019-11-14 20:39:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100070</th>\n",
       "      <td>3</td>\n",
       "      <td>3170.11</td>\n",
       "      <td>2019-12-15 19:41:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>user-299869</th>\n",
       "      <td>1</td>\n",
       "      <td>1552.66</td>\n",
       "      <td>2019-12-20 20:05:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299877</th>\n",
       "      <td>2</td>\n",
       "      <td>1032.10</td>\n",
       "      <td>2019-09-09 11:34:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299882</th>\n",
       "      <td>1</td>\n",
       "      <td>509.81</td>\n",
       "      <td>2019-01-20 20:32:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299892</th>\n",
       "      <td>1</td>\n",
       "      <td>2095.26</td>\n",
       "      <td>2019-09-08 21:33:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299895</th>\n",
       "      <td>1</td>\n",
       "      <td>754.13</td>\n",
       "      <td>2019-04-18 15:37:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299896</th>\n",
       "      <td>2</td>\n",
       "      <td>931.02</td>\n",
       "      <td>2019-11-06 14:20:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299907</th>\n",
       "      <td>1</td>\n",
       "      <td>312.70</td>\n",
       "      <td>2019-06-08 11:22:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299910</th>\n",
       "      <td>2</td>\n",
       "      <td>1050.36</td>\n",
       "      <td>2019-12-23 10:25:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299917</th>\n",
       "      <td>1</td>\n",
       "      <td>260.24</td>\n",
       "      <td>2019-08-11 14:03:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299922</th>\n",
       "      <td>1</td>\n",
       "      <td>617.43</td>\n",
       "      <td>2019-10-05 12:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299924</th>\n",
       "      <td>1</td>\n",
       "      <td>627.51</td>\n",
       "      <td>2019-11-16 11:14:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299926</th>\n",
       "      <td>2</td>\n",
       "      <td>755.93</td>\n",
       "      <td>2019-10-25 14:28:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299930</th>\n",
       "      <td>1</td>\n",
       "      <td>645.19</td>\n",
       "      <td>2019-10-25 21:19:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299934</th>\n",
       "      <td>1</td>\n",
       "      <td>1121.70</td>\n",
       "      <td>2019-12-02 10:16:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299938</th>\n",
       "      <td>1</td>\n",
       "      <td>647.99</td>\n",
       "      <td>2019-05-05 19:49:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299940</th>\n",
       "      <td>1</td>\n",
       "      <td>2084.25</td>\n",
       "      <td>2019-05-16 15:35:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299942</th>\n",
       "      <td>1</td>\n",
       "      <td>2872.65</td>\n",
       "      <td>2019-09-14 16:54:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299943</th>\n",
       "      <td>1</td>\n",
       "      <td>140.63</td>\n",
       "      <td>2019-06-08 17:02:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299944</th>\n",
       "      <td>1</td>\n",
       "      <td>571.63</td>\n",
       "      <td>2019-05-08 13:07:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299945</th>\n",
       "      <td>1</td>\n",
       "      <td>1098.21</td>\n",
       "      <td>2019-08-18 19:11:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299954</th>\n",
       "      <td>1</td>\n",
       "      <td>2418.71</td>\n",
       "      <td>2019-08-24 20:33:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299955</th>\n",
       "      <td>1</td>\n",
       "      <td>504.61</td>\n",
       "      <td>2019-05-01 14:57:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299959</th>\n",
       "      <td>1</td>\n",
       "      <td>536.36</td>\n",
       "      <td>2019-01-22 05:06:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299962</th>\n",
       "      <td>1</td>\n",
       "      <td>478.08</td>\n",
       "      <td>2019-10-21 22:38:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299970</th>\n",
       "      <td>1</td>\n",
       "      <td>582.40</td>\n",
       "      <td>2019-02-22 14:15:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299980</th>\n",
       "      <td>1</td>\n",
       "      <td>441.71</td>\n",
       "      <td>2019-10-18 10:53:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299983</th>\n",
       "      <td>1</td>\n",
       "      <td>706.80</td>\n",
       "      <td>2019-12-27 17:57:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299989</th>\n",
       "      <td>2</td>\n",
       "      <td>1685.18</td>\n",
       "      <td>2019-11-11 10:40:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299992</th>\n",
       "      <td>1</td>\n",
       "      <td>508.75</td>\n",
       "      <td>2019-01-01 16:14:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299995</th>\n",
       "      <td>1</td>\n",
       "      <td>479.94</td>\n",
       "      <td>2019-03-30 16:35:12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70592 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             F  orderAmount           orderTime\n",
       "userID                                         \n",
       "user-100000  1      1978.47 2019-10-13 18:46:46\n",
       "user-100003  1       521.60 2019-05-24 13:04:05\n",
       "user-100006  1       466.89 2019-11-14 15:37:19\n",
       "user-100007  1      2178.20 2019-01-14 18:45:35\n",
       "user-100008  1      4949.65 2019-11-16 17:15:03\n",
       "user-100013  1       565.26 2019-09-29 14:07:29\n",
       "user-100014  1       430.34 2019-07-30 12:04:32\n",
       "user-100018  1       453.83 2019-12-24 20:50:52\n",
       "user-100022  2      3899.20 2019-11-16 11:38:28\n",
       "user-100025  1       113.72 2019-07-21 14:38:45\n",
       "user-100027  1       924.18 2019-06-05 15:51:42\n",
       "user-100030  1      2428.86 2019-09-16 18:38:44\n",
       "user-100032  2      1662.67 2019-09-14 21:19:41\n",
       "user-100033  2      7573.07 2019-10-22 14:15:27\n",
       "user-100034  1      2567.65 2019-09-23 23:02:48\n",
       "user-100037  1       690.51 2019-10-12 20:56:30\n",
       "user-100038  1       562.78 2019-05-15 11:09:10\n",
       "user-100042  1      1284.35 2019-08-19 18:52:11\n",
       "user-100046  2      3030.89 2019-11-04 21:47:19\n",
       "user-100047  2       709.90 2019-09-14 14:56:10\n",
       "user-100048  1       743.85 2019-09-03 10:56:12\n",
       "user-100050  1       310.02 2019-03-11 08:25:14\n",
       "user-100054  1       953.79 2019-12-10 17:20:57\n",
       "user-100057  1       598.73 2019-12-04 14:27:40\n",
       "user-100062  1       496.58 2019-02-27 19:47:04\n",
       "user-100063  1       246.78 2019-02-21 15:36:29\n",
       "user-100065  1      1488.90 2019-06-14 23:39:18\n",
       "user-100067  1      1254.56 2019-12-17 23:23:34\n",
       "user-100068  1      3466.88 2019-11-14 20:39:17\n",
       "user-100070  3      3170.11 2019-12-15 19:41:19\n",
       "...         ..          ...                 ...\n",
       "user-299869  1      1552.66 2019-12-20 20:05:57\n",
       "user-299877  2      1032.10 2019-09-09 11:34:35\n",
       "user-299882  1       509.81 2019-01-20 20:32:49\n",
       "user-299892  1      2095.26 2019-09-08 21:33:40\n",
       "user-299895  1       754.13 2019-04-18 15:37:42\n",
       "user-299896  2       931.02 2019-11-06 14:20:45\n",
       "user-299907  1       312.70 2019-06-08 11:22:38\n",
       "user-299910  2      1050.36 2019-12-23 10:25:46\n",
       "user-299917  1       260.24 2019-08-11 14:03:42\n",
       "user-299922  1       617.43 2019-10-05 12:03:07\n",
       "user-299924  1       627.51 2019-11-16 11:14:42\n",
       "user-299926  2       755.93 2019-10-25 14:28:20\n",
       "user-299930  1       645.19 2019-10-25 21:19:48\n",
       "user-299934  1      1121.70 2019-12-02 10:16:45\n",
       "user-299938  1       647.99 2019-05-05 19:49:41\n",
       "user-299940  1      2084.25 2019-05-16 15:35:18\n",
       "user-299942  1      2872.65 2019-09-14 16:54:24\n",
       "user-299943  1       140.63 2019-06-08 17:02:17\n",
       "user-299944  1       571.63 2019-05-08 13:07:35\n",
       "user-299945  1      1098.21 2019-08-18 19:11:40\n",
       "user-299954  1      2418.71 2019-08-24 20:33:29\n",
       "user-299955  1       504.61 2019-05-01 14:57:37\n",
       "user-299959  1       536.36 2019-01-22 05:06:12\n",
       "user-299962  1       478.08 2019-10-21 22:38:31\n",
       "user-299970  1       582.40 2019-02-22 14:15:43\n",
       "user-299980  1       441.71 2019-10-18 10:53:37\n",
       "user-299983  1       706.80 2019-12-27 17:57:11\n",
       "user-299989  2      1685.18 2019-11-11 10:40:08\n",
       "user-299992  1       508.75 2019-01-01 16:14:47\n",
       "user-299995  1       479.94 2019-03-30 16:35:12\n",
       "\n",
       "[70592 rows x 3 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df = order_df.drop(order_df[order_df.chargeback == '是'].index)\n",
    "temp_df['F'] = 1\n",
    "result_df = temp_df.pivot_table(\n",
    "    index = 'userID',\n",
    "    values = ['orderTime','F','orderAmount'],\n",
    "    aggfunc = {\n",
    "        'orderTime':'max',\n",
    "        'F':'sum',\n",
    "        'orderAmount':'sum'   \n",
    "    } \n",
    ")\n",
    "result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F</th>\n",
       "      <th>orderAmount</th>\n",
       "      <th>orderTime</th>\n",
       "      <th>R</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>user-100000</th>\n",
       "      <td>1</td>\n",
       "      <td>1978.47</td>\n",
       "      <td>2019-10-13 18:46:46</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100003</th>\n",
       "      <td>1</td>\n",
       "      <td>521.60</td>\n",
       "      <td>2019-05-24 13:04:05</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100006</th>\n",
       "      <td>1</td>\n",
       "      <td>466.89</td>\n",
       "      <td>2019-11-14 15:37:19</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100007</th>\n",
       "      <td>1</td>\n",
       "      <td>2178.20</td>\n",
       "      <td>2019-01-14 18:45:35</td>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100008</th>\n",
       "      <td>1</td>\n",
       "      <td>4949.65</td>\n",
       "      <td>2019-11-16 17:15:03</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100013</th>\n",
       "      <td>1</td>\n",
       "      <td>565.26</td>\n",
       "      <td>2019-09-29 14:07:29</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100014</th>\n",
       "      <td>1</td>\n",
       "      <td>430.34</td>\n",
       "      <td>2019-07-30 12:04:32</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100018</th>\n",
       "      <td>1</td>\n",
       "      <td>453.83</td>\n",
       "      <td>2019-12-24 20:50:52</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100022</th>\n",
       "      <td>2</td>\n",
       "      <td>3899.20</td>\n",
       "      <td>2019-11-16 11:38:28</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100025</th>\n",
       "      <td>1</td>\n",
       "      <td>113.72</td>\n",
       "      <td>2019-07-21 14:38:45</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100027</th>\n",
       "      <td>1</td>\n",
       "      <td>924.18</td>\n",
       "      <td>2019-06-05 15:51:42</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100030</th>\n",
       "      <td>1</td>\n",
       "      <td>2428.86</td>\n",
       "      <td>2019-09-16 18:38:44</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100032</th>\n",
       "      <td>2</td>\n",
       "      <td>1662.67</td>\n",
       "      <td>2019-09-14 21:19:41</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100033</th>\n",
       "      <td>2</td>\n",
       "      <td>7573.07</td>\n",
       "      <td>2019-10-22 14:15:27</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100034</th>\n",
       "      <td>1</td>\n",
       "      <td>2567.65</td>\n",
       "      <td>2019-09-23 23:02:48</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100037</th>\n",
       "      <td>1</td>\n",
       "      <td>690.51</td>\n",
       "      <td>2019-10-12 20:56:30</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100038</th>\n",
       "      <td>1</td>\n",
       "      <td>562.78</td>\n",
       "      <td>2019-05-15 11:09:10</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100042</th>\n",
       "      <td>1</td>\n",
       "      <td>1284.35</td>\n",
       "      <td>2019-08-19 18:52:11</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100046</th>\n",
       "      <td>2</td>\n",
       "      <td>3030.89</td>\n",
       "      <td>2019-11-04 21:47:19</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100047</th>\n",
       "      <td>2</td>\n",
       "      <td>709.90</td>\n",
       "      <td>2019-09-14 14:56:10</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100048</th>\n",
       "      <td>1</td>\n",
       "      <td>743.85</td>\n",
       "      <td>2019-09-03 10:56:12</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100050</th>\n",
       "      <td>1</td>\n",
       "      <td>310.02</td>\n",
       "      <td>2019-03-11 08:25:14</td>\n",
       "      <td>295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100054</th>\n",
       "      <td>1</td>\n",
       "      <td>953.79</td>\n",
       "      <td>2019-12-10 17:20:57</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100057</th>\n",
       "      <td>1</td>\n",
       "      <td>598.73</td>\n",
       "      <td>2019-12-04 14:27:40</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100062</th>\n",
       "      <td>1</td>\n",
       "      <td>496.58</td>\n",
       "      <td>2019-02-27 19:47:04</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100063</th>\n",
       "      <td>1</td>\n",
       "      <td>246.78</td>\n",
       "      <td>2019-02-21 15:36:29</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100065</th>\n",
       "      <td>1</td>\n",
       "      <td>1488.90</td>\n",
       "      <td>2019-06-14 23:39:18</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100067</th>\n",
       "      <td>1</td>\n",
       "      <td>1254.56</td>\n",
       "      <td>2019-12-17 23:23:34</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100068</th>\n",
       "      <td>1</td>\n",
       "      <td>3466.88</td>\n",
       "      <td>2019-11-14 20:39:17</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100070</th>\n",
       "      <td>3</td>\n",
       "      <td>3170.11</td>\n",
       "      <td>2019-12-15 19:41:19</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299869</th>\n",
       "      <td>1</td>\n",
       "      <td>1552.66</td>\n",
       "      <td>2019-12-20 20:05:57</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299877</th>\n",
       "      <td>2</td>\n",
       "      <td>1032.10</td>\n",
       "      <td>2019-09-09 11:34:35</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299882</th>\n",
       "      <td>1</td>\n",
       "      <td>509.81</td>\n",
       "      <td>2019-01-20 20:32:49</td>\n",
       "      <td>345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299892</th>\n",
       "      <td>1</td>\n",
       "      <td>2095.26</td>\n",
       "      <td>2019-09-08 21:33:40</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299895</th>\n",
       "      <td>1</td>\n",
       "      <td>754.13</td>\n",
       "      <td>2019-04-18 15:37:42</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299896</th>\n",
       "      <td>2</td>\n",
       "      <td>931.02</td>\n",
       "      <td>2019-11-06 14:20:45</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299907</th>\n",
       "      <td>1</td>\n",
       "      <td>312.70</td>\n",
       "      <td>2019-06-08 11:22:38</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299910</th>\n",
       "      <td>2</td>\n",
       "      <td>1050.36</td>\n",
       "      <td>2019-12-23 10:25:46</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299917</th>\n",
       "      <td>1</td>\n",
       "      <td>260.24</td>\n",
       "      <td>2019-08-11 14:03:42</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299922</th>\n",
       "      <td>1</td>\n",
       "      <td>617.43</td>\n",
       "      <td>2019-10-05 12:03:07</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299924</th>\n",
       "      <td>1</td>\n",
       "      <td>627.51</td>\n",
       "      <td>2019-11-16 11:14:42</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299926</th>\n",
       "      <td>2</td>\n",
       "      <td>755.93</td>\n",
       "      <td>2019-10-25 14:28:20</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299930</th>\n",
       "      <td>1</td>\n",
       "      <td>645.19</td>\n",
       "      <td>2019-10-25 21:19:48</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299934</th>\n",
       "      <td>1</td>\n",
       "      <td>1121.70</td>\n",
       "      <td>2019-12-02 10:16:45</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299938</th>\n",
       "      <td>1</td>\n",
       "      <td>647.99</td>\n",
       "      <td>2019-05-05 19:49:41</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299940</th>\n",
       "      <td>1</td>\n",
       "      <td>2084.25</td>\n",
       "      <td>2019-05-16 15:35:18</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299942</th>\n",
       "      <td>1</td>\n",
       "      <td>2872.65</td>\n",
       "      <td>2019-09-14 16:54:24</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299943</th>\n",
       "      <td>1</td>\n",
       "      <td>140.63</td>\n",
       "      <td>2019-06-08 17:02:17</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299944</th>\n",
       "      <td>1</td>\n",
       "      <td>571.63</td>\n",
       "      <td>2019-05-08 13:07:35</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299945</th>\n",
       "      <td>1</td>\n",
       "      <td>1098.21</td>\n",
       "      <td>2019-08-18 19:11:40</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299954</th>\n",
       "      <td>1</td>\n",
       "      <td>2418.71</td>\n",
       "      <td>2019-08-24 20:33:29</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299955</th>\n",
       "      <td>1</td>\n",
       "      <td>504.61</td>\n",
       "      <td>2019-05-01 14:57:37</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299959</th>\n",
       "      <td>1</td>\n",
       "      <td>536.36</td>\n",
       "      <td>2019-01-22 05:06:12</td>\n",
       "      <td>343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299962</th>\n",
       "      <td>1</td>\n",
       "      <td>478.08</td>\n",
       "      <td>2019-10-21 22:38:31</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299970</th>\n",
       "      <td>1</td>\n",
       "      <td>582.40</td>\n",
       "      <td>2019-02-22 14:15:43</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299980</th>\n",
       "      <td>1</td>\n",
       "      <td>441.71</td>\n",
       "      <td>2019-10-18 10:53:37</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299983</th>\n",
       "      <td>1</td>\n",
       "      <td>706.80</td>\n",
       "      <td>2019-12-27 17:57:11</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299989</th>\n",
       "      <td>2</td>\n",
       "      <td>1685.18</td>\n",
       "      <td>2019-11-11 10:40:08</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299992</th>\n",
       "      <td>1</td>\n",
       "      <td>508.75</td>\n",
       "      <td>2019-01-01 16:14:47</td>\n",
       "      <td>364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299995</th>\n",
       "      <td>1</td>\n",
       "      <td>479.94</td>\n",
       "      <td>2019-03-30 16:35:12</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70592 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             F  orderAmount           orderTime    R\n",
       "userID                                              \n",
       "user-100000  1      1978.47 2019-10-13 18:46:46   79\n",
       "user-100003  1       521.60 2019-05-24 13:04:05  221\n",
       "user-100006  1       466.89 2019-11-14 15:37:19   47\n",
       "user-100007  1      2178.20 2019-01-14 18:45:35  351\n",
       "user-100008  1      4949.65 2019-11-16 17:15:03   45\n",
       "user-100013  1       565.26 2019-09-29 14:07:29   93\n",
       "user-100014  1       430.34 2019-07-30 12:04:32  154\n",
       "user-100018  1       453.83 2019-12-24 20:50:52    7\n",
       "user-100022  2      3899.20 2019-11-16 11:38:28   45\n",
       "user-100025  1       113.72 2019-07-21 14:38:45  163\n",
       "user-100027  1       924.18 2019-06-05 15:51:42  209\n",
       "user-100030  1      2428.86 2019-09-16 18:38:44  106\n",
       "user-100032  2      1662.67 2019-09-14 21:19:41  108\n",
       "user-100033  2      7573.07 2019-10-22 14:15:27   70\n",
       "user-100034  1      2567.65 2019-09-23 23:02:48   99\n",
       "user-100037  1       690.51 2019-10-12 20:56:30   80\n",
       "user-100038  1       562.78 2019-05-15 11:09:10  230\n",
       "user-100042  1      1284.35 2019-08-19 18:52:11  134\n",
       "user-100046  2      3030.89 2019-11-04 21:47:19   57\n",
       "user-100047  2       709.90 2019-09-14 14:56:10  108\n",
       "user-100048  1       743.85 2019-09-03 10:56:12  119\n",
       "user-100050  1       310.02 2019-03-11 08:25:14  295\n",
       "user-100054  1       953.79 2019-12-10 17:20:57   21\n",
       "user-100057  1       598.73 2019-12-04 14:27:40   27\n",
       "user-100062  1       496.58 2019-02-27 19:47:04  307\n",
       "user-100063  1       246.78 2019-02-21 15:36:29  313\n",
       "user-100065  1      1488.90 2019-06-14 23:39:18  200\n",
       "user-100067  1      1254.56 2019-12-17 23:23:34   14\n",
       "user-100068  1      3466.88 2019-11-14 20:39:17   47\n",
       "user-100070  3      3170.11 2019-12-15 19:41:19   16\n",
       "...         ..          ...                 ...  ...\n",
       "user-299869  1      1552.66 2019-12-20 20:05:57   11\n",
       "user-299877  2      1032.10 2019-09-09 11:34:35  113\n",
       "user-299882  1       509.81 2019-01-20 20:32:49  345\n",
       "user-299892  1      2095.26 2019-09-08 21:33:40  114\n",
       "user-299895  1       754.13 2019-04-18 15:37:42  257\n",
       "user-299896  2       931.02 2019-11-06 14:20:45   55\n",
       "user-299907  1       312.70 2019-06-08 11:22:38  206\n",
       "user-299910  2      1050.36 2019-12-23 10:25:46    8\n",
       "user-299917  1       260.24 2019-08-11 14:03:42  142\n",
       "user-299922  1       617.43 2019-10-05 12:03:07   87\n",
       "user-299924  1       627.51 2019-11-16 11:14:42   45\n",
       "user-299926  2       755.93 2019-10-25 14:28:20   67\n",
       "user-299930  1       645.19 2019-10-25 21:19:48   67\n",
       "user-299934  1      1121.70 2019-12-02 10:16:45   29\n",
       "user-299938  1       647.99 2019-05-05 19:49:41  240\n",
       "user-299940  1      2084.25 2019-05-16 15:35:18  229\n",
       "user-299942  1      2872.65 2019-09-14 16:54:24  108\n",
       "user-299943  1       140.63 2019-06-08 17:02:17  206\n",
       "user-299944  1       571.63 2019-05-08 13:07:35  237\n",
       "user-299945  1      1098.21 2019-08-18 19:11:40  135\n",
       "user-299954  1      2418.71 2019-08-24 20:33:29  129\n",
       "user-299955  1       504.61 2019-05-01 14:57:37  244\n",
       "user-299959  1       536.36 2019-01-22 05:06:12  343\n",
       "user-299962  1       478.08 2019-10-21 22:38:31   71\n",
       "user-299970  1       582.40 2019-02-22 14:15:43  312\n",
       "user-299980  1       441.71 2019-10-18 10:53:37   74\n",
       "user-299983  1       706.80 2019-12-27 17:57:11    4\n",
       "user-299989  2      1685.18 2019-11-11 10:40:08   50\n",
       "user-299992  1       508.75 2019-01-01 16:14:47  364\n",
       "user-299995  1       479.94 2019-03-30 16:35:12  276\n",
       "\n",
       "[70592 rows x 4 columns]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "curr_date = datetime(2019, 12, 31, 23, 59, 59)\n",
    "\n",
    "# 计算最后一次购买距离2019年的最后一天相差了多少天\n",
    "result_df['R'] = (curr_date - result_df.orderTime).dt.days\n",
    "result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32623.050000000003 6.1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17da496ac88>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "print(result_df.orderAmount.max(), result_df.orderAmount.min())\n",
    "# 检查R字段的分布\n",
    "sns.kdeplot(result_df.orderAmount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "364 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17da4454320>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "print(result_df.R.max(), result_df.R.min())\n",
    "# 检查R字段的分布\n",
    "sns.kdeplot(result_df.R)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17da18ea630>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "print(result_df.F.max(), result_df.F.min())\n",
    "# 检查F字段的分布\n",
    "sns.distplot(result_df.F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "               R  F        M\n",
       "userID                      \n",
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       "user-100006   47  1   466.89\n",
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       "user-100022   45  2  3899.20\n",
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       "user-100032  108  2  1662.67\n",
       "user-100033   70  2  7573.07\n",
       "user-100034   99  1  2567.65\n",
       "user-100037   80  1   690.51\n",
       "user-100038  230  1   562.78\n",
       "user-100042  134  1  1284.35\n",
       "user-100046   57  2  3030.89\n",
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       "user-100048  119  1   743.85\n",
       "user-100050  295  1   310.02\n",
       "user-100054   21  1   953.79\n",
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       "user-100063  313  1   246.78\n",
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       "user-100067   14  1  1254.56\n",
       "user-100068   47  1  3466.88\n",
       "user-100070   16  3  3170.11\n",
       "...          ... ..      ...\n",
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       "user-299882  345  1   509.81\n",
       "user-299892  114  1  2095.26\n",
       "user-299895  257  1   754.13\n",
       "user-299896   55  2   931.02\n",
       "user-299907  206  1   312.70\n",
       "user-299910    8  2  1050.36\n",
       "user-299917  142  1   260.24\n",
       "user-299922   87  1   617.43\n",
       "user-299924   45  1   627.51\n",
       "user-299926   67  2   755.93\n",
       "user-299930   67  1   645.19\n",
       "user-299934   29  1  1121.70\n",
       "user-299938  240  1   647.99\n",
       "user-299940  229  1  2084.25\n",
       "user-299942  108  1  2872.65\n",
       "user-299943  206  1   140.63\n",
       "user-299944  237  1   571.63\n",
       "user-299945  135  1  1098.21\n",
       "user-299954  129  1  2418.71\n",
       "user-299955  244  1   504.61\n",
       "user-299959  343  1   536.36\n",
       "user-299962   71  1   478.08\n",
       "user-299970  312  1   582.40\n",
       "user-299980   74  1   441.71\n",
       "user-299983    4  1   706.80\n",
       "user-299989   50  2  1685.18\n",
       "user-299992  364  1   508.75\n",
       "user-299995  276  1   479.94\n",
       "\n",
       "[70592 rows x 3 columns]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df['M'] = result_df.orderAmount\n",
    "result_df = result_df.reindex(columns=['R','F','M'])\n",
    "result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>user-100027</th>\n",
       "      <td>60.077573</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-408.044463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100030</th>\n",
       "      <td>-42.922427</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>1096.635537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100032</th>\n",
       "      <td>-40.922427</td>\n",
       "      <td>0.729176</td>\n",
       "      <td>330.445537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100033</th>\n",
       "      <td>-78.922427</td>\n",
       "      <td>0.729176</td>\n",
       "      <td>6240.845537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100034</th>\n",
       "      <td>-49.922427</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>1235.425537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100037</th>\n",
       "      <td>-68.922427</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-641.714463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100038</th>\n",
       "      <td>81.077573</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-769.444463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100042</th>\n",
       "      <td>-14.922427</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-47.874463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100046</th>\n",
       "      <td>-91.922427</td>\n",
       "      <td>0.729176</td>\n",
       "      <td>1698.665537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100047</th>\n",
       "      <td>-40.922427</td>\n",
       "      <td>0.729176</td>\n",
       "      <td>-622.324463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100048</th>\n",
       "      <td>-29.922427</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-588.374463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100050</th>\n",
       "      <td>146.077573</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-1022.204463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100054</th>\n",
       "      <td>-127.922427</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-378.434463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100057</th>\n",
       "      <td>-121.922427</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-733.494463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100062</th>\n",
       "      <td>158.077573</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-835.644463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100063</th>\n",
       "      <td>164.077573</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-1085.444463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100065</th>\n",
       "      <td>51.077573</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>156.675537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100067</th>\n",
       "      <td>-134.922427</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-77.664463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100068</th>\n",
       "      <td>-101.922427</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>2134.655537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100070</th>\n",
       "      <td>-132.922427</td>\n",
       "      <td>1.729176</td>\n",
       "      <td>1837.885537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299869</th>\n",
       "      <td>-137.922427</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>220.435537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299877</th>\n",
       "      <td>-35.922427</td>\n",
       "      <td>0.729176</td>\n",
       "      <td>-300.124463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299882</th>\n",
       "      <td>196.077573</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-822.414463</td>\n",
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       "    <tr>\n",
       "      <th>user-299892</th>\n",
       "      <td>-34.922427</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>763.035537</td>\n",
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       "    <tr>\n",
       "      <th>user-299895</th>\n",
       "      <td>108.077573</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-578.094463</td>\n",
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       "    <tr>\n",
       "      <th>user-299896</th>\n",
       "      <td>-93.922427</td>\n",
       "      <td>0.729176</td>\n",
       "      <td>-401.204463</td>\n",
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       "    <tr>\n",
       "      <th>user-299907</th>\n",
       "      <td>57.077573</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-1019.524463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299910</th>\n",
       "      <td>-140.922427</td>\n",
       "      <td>0.729176</td>\n",
       "      <td>-281.864463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299917</th>\n",
       "      <td>-6.922427</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-1071.984463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299922</th>\n",
       "      <td>-61.922427</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-714.794463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299924</th>\n",
       "      <td>-103.922427</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-704.714463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299926</th>\n",
       "      <td>-81.922427</td>\n",
       "      <td>0.729176</td>\n",
       "      <td>-576.294463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299930</th>\n",
       "      <td>-81.922427</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-687.034463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299934</th>\n",
       "      <td>-119.922427</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-210.524463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299938</th>\n",
       "      <td>91.077573</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-684.234463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299940</th>\n",
       "      <td>80.077573</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>752.025537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299942</th>\n",
       "      <td>-40.922427</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>1540.425537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299943</th>\n",
       "      <td>57.077573</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-1191.594463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299944</th>\n",
       "      <td>88.077573</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-760.594463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299945</th>\n",
       "      <td>-13.922427</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-234.014463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299954</th>\n",
       "      <td>-19.922427</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>1086.485537</td>\n",
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       "    <tr>\n",
       "      <th>user-299955</th>\n",
       "      <td>95.077573</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-827.614463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299959</th>\n",
       "      <td>194.077573</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-795.864463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299962</th>\n",
       "      <td>-77.922427</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-854.144463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299970</th>\n",
       "      <td>163.077573</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-749.824463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299980</th>\n",
       "      <td>-74.922427</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-890.514463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299983</th>\n",
       "      <td>-144.922427</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-625.424463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299989</th>\n",
       "      <td>-98.922427</td>\n",
       "      <td>0.729176</td>\n",
       "      <td>352.955537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299992</th>\n",
       "      <td>215.077573</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-823.474463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299995</th>\n",
       "      <td>127.077573</td>\n",
       "      <td>-0.270824</td>\n",
       "      <td>-852.284463</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70592 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      R         F            M\n",
       "userID                                        \n",
       "user-100000  -69.922427 -0.270824   646.245537\n",
       "user-100003   72.077573 -0.270824  -810.624463\n",
       "user-100006 -101.922427 -0.270824  -865.334463\n",
       "user-100007  202.077573 -0.270824   845.975537\n",
       "user-100008 -103.922427 -0.270824  3617.425537\n",
       "user-100013  -55.922427 -0.270824  -766.964463\n",
       "user-100014    5.077573 -0.270824  -901.884463\n",
       "user-100018 -141.922427 -0.270824  -878.394463\n",
       "user-100022 -103.922427  0.729176  2566.975537\n",
       "user-100025   14.077573 -0.270824 -1218.504463\n",
       "user-100027   60.077573 -0.270824  -408.044463\n",
       "user-100030  -42.922427 -0.270824  1096.635537\n",
       "user-100032  -40.922427  0.729176   330.445537\n",
       "user-100033  -78.922427  0.729176  6240.845537\n",
       "user-100034  -49.922427 -0.270824  1235.425537\n",
       "user-100037  -68.922427 -0.270824  -641.714463\n",
       "user-100038   81.077573 -0.270824  -769.444463\n",
       "user-100042  -14.922427 -0.270824   -47.874463\n",
       "user-100046  -91.922427  0.729176  1698.665537\n",
       "user-100047  -40.922427  0.729176  -622.324463\n",
       "user-100048  -29.922427 -0.270824  -588.374463\n",
       "user-100050  146.077573 -0.270824 -1022.204463\n",
       "user-100054 -127.922427 -0.270824  -378.434463\n",
       "user-100057 -121.922427 -0.270824  -733.494463\n",
       "user-100062  158.077573 -0.270824  -835.644463\n",
       "user-100063  164.077573 -0.270824 -1085.444463\n",
       "user-100065   51.077573 -0.270824   156.675537\n",
       "user-100067 -134.922427 -0.270824   -77.664463\n",
       "user-100068 -101.922427 -0.270824  2134.655537\n",
       "user-100070 -132.922427  1.729176  1837.885537\n",
       "...                 ...       ...          ...\n",
       "user-299869 -137.922427 -0.270824   220.435537\n",
       "user-299877  -35.922427  0.729176  -300.124463\n",
       "user-299882  196.077573 -0.270824  -822.414463\n",
       "user-299892  -34.922427 -0.270824   763.035537\n",
       "user-299895  108.077573 -0.270824  -578.094463\n",
       "user-299896  -93.922427  0.729176  -401.204463\n",
       "user-299907   57.077573 -0.270824 -1019.524463\n",
       "user-299910 -140.922427  0.729176  -281.864463\n",
       "user-299917   -6.922427 -0.270824 -1071.984463\n",
       "user-299922  -61.922427 -0.270824  -714.794463\n",
       "user-299924 -103.922427 -0.270824  -704.714463\n",
       "user-299926  -81.922427  0.729176  -576.294463\n",
       "user-299930  -81.922427 -0.270824  -687.034463\n",
       "user-299934 -119.922427 -0.270824  -210.524463\n",
       "user-299938   91.077573 -0.270824  -684.234463\n",
       "user-299940   80.077573 -0.270824   752.025537\n",
       "user-299942  -40.922427 -0.270824  1540.425537\n",
       "user-299943   57.077573 -0.270824 -1191.594463\n",
       "user-299944   88.077573 -0.270824  -760.594463\n",
       "user-299945  -13.922427 -0.270824  -234.014463\n",
       "user-299954  -19.922427 -0.270824  1086.485537\n",
       "user-299955   95.077573 -0.270824  -827.614463\n",
       "user-299959  194.077573 -0.270824  -795.864463\n",
       "user-299962  -77.922427 -0.270824  -854.144463\n",
       "user-299970  163.077573 -0.270824  -749.824463\n",
       "user-299980  -74.922427 -0.270824  -890.514463\n",
       "user-299983 -144.922427 -0.270824  -625.424463\n",
       "user-299989  -98.922427  0.729176   352.955537\n",
       "user-299992  215.077573 -0.270824  -823.474463\n",
       "user-299995  127.077573 -0.270824  -852.284463\n",
       "\n",
       "[70592 rows x 3 columns]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 通过跟均值比大小判断RFM三个值是高还是低\n",
    "temp_df = result_df.apply(lambda x: x - x.median())\n",
    "temp_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>R</th>\n",
       "      <th>F</th>\n",
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       "    <tr>\n",
       "      <th>user-100006</th>\n",
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       "    <tr>\n",
       "      <th>user-100007</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>user-100008</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100013</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>user-100014</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>user-100018</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>user-100022</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100025</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100027</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>user-100030</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>user-100032</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100033</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100034</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100037</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100038</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100042</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100046</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100047</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100048</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100050</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100054</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100057</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100062</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100063</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100065</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100067</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100068</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100070</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299869</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299877</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299882</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299892</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299895</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299896</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299907</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299910</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299917</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299922</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299924</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299926</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299930</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299934</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299938</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299940</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299942</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299943</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299944</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299945</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299954</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299955</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299959</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299962</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299970</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299980</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299983</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299989</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299992</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299995</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70592 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             R  F  M\n",
       "userID              \n",
       "user-100000  0  0  1\n",
       "user-100003  1  0  0\n",
       "user-100006  0  0  0\n",
       "user-100007  1  0  1\n",
       "user-100008  0  0  1\n",
       "user-100013  0  0  0\n",
       "user-100014  1  0  0\n",
       "user-100018  0  0  0\n",
       "user-100022  0  1  1\n",
       "user-100025  1  0  0\n",
       "user-100027  1  0  0\n",
       "user-100030  0  0  1\n",
       "user-100032  0  1  1\n",
       "user-100033  0  1  1\n",
       "user-100034  0  0  1\n",
       "user-100037  0  0  0\n",
       "user-100038  1  0  0\n",
       "user-100042  0  0  0\n",
       "user-100046  0  1  1\n",
       "user-100047  0  1  0\n",
       "user-100048  0  0  0\n",
       "user-100050  1  0  0\n",
       "user-100054  0  0  0\n",
       "user-100057  0  0  0\n",
       "user-100062  1  0  0\n",
       "user-100063  1  0  0\n",
       "user-100065  1  0  1\n",
       "user-100067  0  0  0\n",
       "user-100068  0  0  1\n",
       "user-100070  0  1  1\n",
       "...         .. .. ..\n",
       "user-299869  0  0  1\n",
       "user-299877  0  1  0\n",
       "user-299882  1  0  0\n",
       "user-299892  0  0  1\n",
       "user-299895  1  0  0\n",
       "user-299896  0  1  0\n",
       "user-299907  1  0  0\n",
       "user-299910  0  1  0\n",
       "user-299917  0  0  0\n",
       "user-299922  0  0  0\n",
       "user-299924  0  0  0\n",
       "user-299926  0  1  0\n",
       "user-299930  0  0  0\n",
       "user-299934  0  0  0\n",
       "user-299938  1  0  0\n",
       "user-299940  1  0  1\n",
       "user-299942  0  0  1\n",
       "user-299943  1  0  0\n",
       "user-299944  1  0  0\n",
       "user-299945  0  0  0\n",
       "user-299954  0  0  1\n",
       "user-299955  1  0  0\n",
       "user-299959  1  0  0\n",
       "user-299962  0  0  0\n",
       "user-299970  1  0  0\n",
       "user-299980  0  0  0\n",
       "user-299983  0  0  0\n",
       "user-299989  0  1  1\n",
       "user-299992  1  0  0\n",
       "user-299995  1  0  0\n",
       "\n",
       "[70592 rows x 3 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_model = temp_df.applymap(lambda x: '1' if x >= 0 else '0')\n",
    "rfm_model['tag'] = rfm_model.apply(make_tag, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>TAG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>user-100000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>重要挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100003</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100006</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100007</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>重要发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100008</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>重要挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100013</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100014</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100018</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100022</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>重要保持用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100025</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100027</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100030</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>重要挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100032</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>重要保持用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100033</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>重要保持用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100034</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>重要挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100037</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100038</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100042</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100046</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>重要保持用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100047</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>一般保持用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100048</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100050</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100054</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100057</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100062</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100063</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100065</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>重要发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100067</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100068</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>重要挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100070</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>重要保持用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299869</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>重要挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299877</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>一般保持用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299882</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299892</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>重要挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299895</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299896</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>一般保持用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299907</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299910</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>一般保持用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299917</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299922</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299924</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299926</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>一般保持用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299930</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299934</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299938</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299940</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>重要发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299942</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>重要挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299943</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299944</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299945</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299954</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>重要挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299955</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299959</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299962</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299970</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299980</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299983</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299989</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>重要保持用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299992</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299995</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70592 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             R  F  M     TAG\n",
       "userID                      \n",
       "user-100000  0  0  1  重要挽留用户\n",
       "user-100003  1  0  0  一般发展用户\n",
       "user-100006  0  0  0  一般挽留用户\n",
       "user-100007  1  0  1  重要发展用户\n",
       "user-100008  0  0  1  重要挽留用户\n",
       "user-100013  0  0  0  一般挽留用户\n",
       "user-100014  1  0  0  一般发展用户\n",
       "user-100018  0  0  0  一般挽留用户\n",
       "user-100022  0  1  1  重要保持用户\n",
       "user-100025  1  0  0  一般发展用户\n",
       "user-100027  1  0  0  一般发展用户\n",
       "user-100030  0  0  1  重要挽留用户\n",
       "user-100032  0  1  1  重要保持用户\n",
       "user-100033  0  1  1  重要保持用户\n",
       "user-100034  0  0  1  重要挽留用户\n",
       "user-100037  0  0  0  一般挽留用户\n",
       "user-100038  1  0  0  一般发展用户\n",
       "user-100042  0  0  0  一般挽留用户\n",
       "user-100046  0  1  1  重要保持用户\n",
       "user-100047  0  1  0  一般保持用户\n",
       "user-100048  0  0  0  一般挽留用户\n",
       "user-100050  1  0  0  一般发展用户\n",
       "user-100054  0  0  0  一般挽留用户\n",
       "user-100057  0  0  0  一般挽留用户\n",
       "user-100062  1  0  0  一般发展用户\n",
       "user-100063  1  0  0  一般发展用户\n",
       "user-100065  1  0  1  重要发展用户\n",
       "user-100067  0  0  0  一般挽留用户\n",
       "user-100068  0  0  1  重要挽留用户\n",
       "user-100070  0  1  1  重要保持用户\n",
       "...         .. .. ..     ...\n",
       "user-299869  0  0  1  重要挽留用户\n",
       "user-299877  0  1  0  一般保持用户\n",
       "user-299882  1  0  0  一般发展用户\n",
       "user-299892  0  0  1  重要挽留用户\n",
       "user-299895  1  0  0  一般发展用户\n",
       "user-299896  0  1  0  一般保持用户\n",
       "user-299907  1  0  0  一般发展用户\n",
       "user-299910  0  1  0  一般保持用户\n",
       "user-299917  0  0  0  一般挽留用户\n",
       "user-299922  0  0  0  一般挽留用户\n",
       "user-299924  0  0  0  一般挽留用户\n",
       "user-299926  0  1  0  一般保持用户\n",
       "user-299930  0  0  0  一般挽留用户\n",
       "user-299934  0  0  0  一般挽留用户\n",
       "user-299938  1  0  0  一般发展用户\n",
       "user-299940  1  0  1  重要发展用户\n",
       "user-299942  0  0  1  重要挽留用户\n",
       "user-299943  1  0  0  一般发展用户\n",
       "user-299944  1  0  0  一般发展用户\n",
       "user-299945  0  0  0  一般挽留用户\n",
       "user-299954  0  0  1  重要挽留用户\n",
       "user-299955  1  0  0  一般发展用户\n",
       "user-299959  1  0  0  一般发展用户\n",
       "user-299962  0  0  0  一般挽留用户\n",
       "user-299970  1  0  0  一般发展用户\n",
       "user-299980  0  0  0  一般挽留用户\n",
       "user-299983  0  0  0  一般挽留用户\n",
       "user-299989  0  1  1  重要保持用户\n",
       "user-299992  1  0  0  一般发展用户\n",
       "user-299995  1  0  0  一般发展用户\n",
       "\n",
       "[70592 rows x 4 columns]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_dict = {\n",
    "    '111': '重要价值用户',\n",
    "    '101': '重要发展用户',\n",
    "    '011': '重要保持用户',\n",
    "    '001': '重要挽留用户',\n",
    "    '110': '一般价值用户',\n",
    "    '100': '一般发展用户',\n",
    "    '010': '一般保持用户',\n",
    "    '000': '一般挽留用户'\n",
    "}\n",
    "\n",
    "\n",
    "def make_tag(model):\n",
    "    key = model['R'] + model['F'] + model['M']\n",
    "    return tags_dict[key]\n",
    "\n",
    "\n",
    "# 根据RFM三个列的值标记0和1\n",
    "rfm_model = temp_df.applymap(lambda x: '1' if x >= 0 else '0')\n",
    "# 根据RFM值给用户添加标签\n",
    "rfm_model['TAG'] = rfm_model.apply(make_tag, axis=1)\n",
    "rfm_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17dafe4e978>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ser = rfm_model.groupby('TAG').TAG.count()\n",
    "ser.plot(kind='pie', autopct='%.1f%%', fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}
